“Data Driven Thinking” is a column written by members of the media community and containing fresh ideas on the digital revolution in media.
Today’s column is written by Pascal Bensoussan, VP of Products at Aggregate Knowledge, a buy-side optimization platform.
This is Part 1 of a 2-part series. In Part 1, I will make the case for why agencies and large advertisers should spend more time thinking of deploying a media buying architecture that can accommodate multiple Demand-Side Platforms (DSPs). In Part 2 (my next post), I will provide specific guidelines on how they can do it.
Today, there are about 8 billion impressions available daily on RTB-enabled ad exchanges (I am excluding ~5 billion impressions from Right Media since it is not yet RTB-enabled) and this number is expected to double or triple in the next 24 months. That means 300,000 biddable impressions every second, with peaks at 3x-7x in the order of 1 to 2 million impressions per second. That’s a lot of biddable inventory that a real-time bidder would have to scan through to cherry pick impressions!
Here is the catch. Most DSPs operate in the 6,000 to 12,000 bid requests per second range, a fraction of the inventory available. This number will certainly grow, but not as fast as the inventory available on the exchanges.
Don’t expect full visibility into all biddable impressions from one single buying platform anytime soon
I don’t expect DSPs to achieve much greater scale in the near future for multiple reasons:
- Early adopters of real-time bidders want cheaper access to larger pools of targeted inventory. As such, they use DSPs as retargeting platforms, *not* as trading platforms. An illustrative example can help make the point. Take a campaign delivering 100 million impressions to retargeted cookies over 4 weeks. That means buying an average of 40 impressions per second, far from the hundreds of thousands available.
- RTB-enabled ad exchanges and DSPs are just getting started. Their technology is relatively young and they are still toying with very exploratory bidding and optimization algorithms (if any). None of them have the large-scale experience and the analytical infrastructure to draw reliable and statistically significant lines separating the good inventory from the bad inventory by campaign, by audience segment, and by publisher.
- Retargeted inventory is not differentiated beyond standard dimensions such as time of day, day of week, ISP, or connection speed, which means that the grid to scan inventory is still too coarse to warrant accessing larger pools of seemingly equivalent inventory.
- Volume of biddable inventory will keep growing faster than the technology capable of scanning all of it. Although raw talent is absolutely necessary, it takes a lot of resources to build a scalable infrastructure capable of processing up to a million impressions per second. So far, DSPs have not had the time or the money for this.
Be prepared to test and use multiple demand-side buying platforms in parallel
For agencies, betting on one DSP so early in the game involves a significant risk, particularly:
- Coverage risk: DSPs will only access 5 to 15 percent of all biddable impressions available on ad exchanges every second.
- Platform/technology risk: There is no clear winner, with platforms still evolving significantly, and new DSPs entering the market monthly.
- Competitive risk: Hedge your bets against DSPs being acquired by a direct competitor or by your “frenemy” or if they start competing directly with you. In his latest post, Rise of the Demand-Side Service Layer, Michael Walrath writes that even though their relationship is very symbiotic today, DSPs may come to look more like today’s digital agencies, while digital agencies will act more like DSPs.
- CPM risk: The more clients DSPs have, the higher they will pay to win exchange impressions. Google must love that! This is a direct result of the having more demand compete for the same thin slice of inventory with an optimization grid to coarse to differentiate anyone bidder. Demand Side Platforms are not set up to protect individual clients by scanning separate pools of bid requests (each at a rate of 6K to 12K per second) for the exclusive benefit of each client individually. Instead, they scan a single pool of bid requests and decide which agency, advertiser, or campaign, competing for the same impression, wins the bid based on bid parameters, pacing constraints, and inventory quality. Is this fundamentally different from how an ad network arbitrates media across competing sources of demand?
To increase inventory coverage, minimize platform/technology risk, and better position themselves for future arbitrage opportunities, agencies should be prepared to deploy a media buying architecture that is able to support multiple concurrent DSPs with low switching costs.
Understand what it takes to deploy a multi-DSP business and system architecture
Implementing a business/system architecture that supports multiple DSPs is not easy. There are numerous challenges to overcome. Here is a list of six critical goals that you should set for yourself as you embark into this journey:
- Think plug-in from the get-go: Define a plug-in architecture allowing low switching costs across data providers, media channels (including direct publishers, ad networks, and DSPs) and dynamic creative vendors without silo-ing the “who” (your audience), the “where” (your media), and the “what” (your creative).
- Centralize audience management: Have a coherent data strategy across all data providers and centralized audience management across all media channels (including all DSPs).
- Remove frictions across multiple media channels: Deploy your DSPs partners in a way that minimizes duplicate work, allows for global frequency capping, and avoids overpaying for exchange inventory.
- Track every penny, whether spent on data or media: Implement a robust accounting system to track meticulously and allocate all your media buying and data buying costs whether you make bulk buys across advertisers or campaign-specific buys.
- Unify reporting: Integrate your own ad server data (the only one that really counts) with detailed user profiles and a complete picture of all your data and media buying costs.
- Close the loop: Define your our own attribution models and the key metrics allowing audience ROI analysis, campaign delivery tracking, and apple-to-apple comparisons across all your data providers and media channels.
Look for specific guidelines on how to address those challenges in my next post on AdExchanger.com. In the meantime, I would love to hear your thoughts on this topic.
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